The analysis of transformer's condition based on Bayes-discriminant method with fault-tree fuzzy evaluation

• Autorzy: Li Dianyang , Wang Shanyuan , Zhang Yujie , Feng Jian , Wang Hongzhe , Qin Ling

Identyfikatory

DOI

10.29354/diag/118653

• Języki publikacji: EN

Abstrakty

EN

China's electric power construction is renewing Increasingly, and the network is complex and changeable where the automation is getting higher. In this paper, Fuzzy evaluation system is established according to fault tree, and the estimation of transformer’s state is judged by analytic hierarchy process. Bayes-discriminant and discriminant formula are used to discriminate transformer’s attributes, which are based on historical data. The machine identification of transformer faults combines the fuzzy evaluation and Bayes-discriminant. It’s accuracy can be improved by correcting parameters. This method can effectively avoid subjective interference caused by artificial weights. The example shows that this method could be applied to judge health status of electric power equipment and this method can play an early-warning role in the operation of monitoring system.

Słowa kluczowe

EN

oil-immersed power transformers; fault tree analysis; fuzzy-evaluation system; Bayes-discriminant; health-operation

PL

transformator mocy; system monitorowania; analiza drzewa błędów

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2020
• Tom: Vol. 21, No. 2
• Strony: 3--12
• Opis fizyczny: Bibliogr. 15 poz., rys., tab.

Twórcy

autor

Li Dianyang

• Northeastern University,College Of Information Science and Engineering, 110004
• State Grid liaoning Electric Power Co, 110004

autor

Wang Shanyuan

• Northeastern University,College Of Information Science and Engineering, 110004

autor

Zhang Yujie

• Northeastern University,College Of Information Science and Engineering, 110004

autor

Feng Jian

• Northeastern University,College Of Information Science and Engineering, 110004

autor

Wang Hongzhe

• State Grid liaoning Electric Power Co, 110004

autor

Qin Ling

• State Grid liaoning Electric Power Co, 110004

Bibliografia

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• 3. Chiacchio F, D'Urso D, Compagno L, Pennisi M, Pappalardo F, Manno G. A stochastic hybrid fault tree automaton for the modelling and simulation of dynamic reliability problems. Expert Systems with Applications. 2016;47(3):42-57. https://doi.org/10.1016/j.eswa.2015.10.046
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• 12. Biswas SS, Srivastava AK, Whitehead D. A RealTime Data-Driven Algorithm for Health Diagnosis and Prognosis of a Circuit Breaker Trip Assembly. IEEE Transactions on Industrial Electronics. 2015; 62(6):3822-3831. https://doi.org/10.1109/TIE.2014.2362498
• 13. Duval M. A review of faults detectable by gas-in-oil analysis in transformers. IEEE Electrical Insulation Magazine.2002;18(3):8-17. https://doi.org/10.1109/MEI.2002.1014963
• 14. Dai J, Song H, Sheng G, Jiang X. Dissolved gas analysis of insulating oil for power transformer fault diagnosis with deep belief network. IEEE Transactions on Dielectrics and Electrical Insulation. 2017;24(5)2828-2835 . https://doi.org/10.1109/TDEI.2017.006727
• 15. He X, Ai Q, Qiu RC, Huang W, Piao L, Liu H. A Big Data Architecture Design for Smart Grids Based on Random Matrix Theory. IEEE Transactions on Smart Grid.2017;8(2):674-686. https://doi.org/10.1109/TSG.2015.2445828

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).